In today’s technology-driven business landscape, many organizations understand that data is the currency of digital transformation – yet very few have the foresight to look beyond the traditional sources of information we’re accustomed to. Focus tends to rest solely on the transactional nature of business as we know it and in the age of “disrupt or be disrupted,” stagnation does not bode well.

New sources of data are appearing continuously, and these new sources often don’t look anything like what they have seen or dealt with in the past. As advancements in technology and the evolving needs of the customer have created new opportunities and challenges for enterprises, the combination of existing data with new sources of information into a single pane of glass will become increasingly vital.

Part of me wonders why we focus our trend and market predictions at the beginning and end of each year, when we all know these changes are fluid, and don’t happen according to a perfect calendar date. But it’s important to constantly evaluate the changes we are seeing, and understand where they will take us next. And in that spirit, I’d like to share three market predictions my team and I believe will come to pass in 2018 and beyond.

1. Expectations for database systems have expanded beyond relational to include alternative models.

Non-relational database technology, such as NoSQL and Hadoop, have emerged over the last few years. However, now the expectation is that leading database platforms can provide a wider range of capabilities and address the broader range of use cases and workloads that these non-relational technologies have enabled.

Now that the big game has come and gone for another year, I have to admit that as I watched the Patriots and the Falcons duke it out, I started realizing how much machine learning (ML) and artificial intelligence can be applied to our sports culture. If you read my 2017 predictions blog, you’ll know that I see major implications for ML in the coming years. So let’s take a look at how we use technology in sports and fan engagement, and how that can translate to the enterprise.

Who’s choosing who’s on first?

Scouting in sports is a human pursuit. It takes a highly skilled, observant and excellent judge to watch an athlete perform at an amateur level, and understand if he or she has what it takes to go pro. But what if a machine could do that just as well – if not better – than a human? With wearable technologies, data is available to show us every bit of relevant information about an athlete – heart rate, muscle mass, bone density…the list goes on. By analyzing that information, compiled with broader data, like current team lineup and weaknesses, a machine could conceivably review dozens of athletes to determine which would make the best fit in the organization. You could argue that this is still a field that relies too much on gut instinct to ever fully transform to a machine. But it’s not impossible to imagine machine learning playing a much bigger role in recruitment and team dynamics in the future.

After the ball drops in Times Square and the New Year has been rung in, many turn to reflection – on the year past of course, but also on how to make the next one even better than the last. This is especially true for business leaders who often use the time for reflection on the things they did right, what could have been better and the competitive opportunities for the coming year.

2016 saw more cloud adoption than ever before – it’s no secret that the way we do business is changing, and that’s exactly why in 2017 businesses need to be even more digitally savvy. With data being created at a breakneck pace – from connected ‘things’, to mobile payments and more – businesses are uniquely positioned to be more informed than ever before and make better, data-driven, decisions.